Methods and systems for image analysis identification

ABSTRACT

A computer-implemented method for identifying a first object-of-interest is provided. The first object-of-interest includes two identifiers and a sample portion. The method includes imaging the first object-of-interest including the two identifiers. The imaging generates a first set of image data. The method further includes determining a position of the first object-of-interest in the field-of-view of an optical sensor and determining the two identifiers from the first set of image data. The method includes identifying the first object-of-interest based on the two identifiers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. provisionalapplication Ser. No. 61/541,453, filed Sep. 30, 2011, which isincorporated herein by reference in its entirety.

BACKGROUND

Generally, there is an increasing need to automate systems to increaseefficiency. For example, advances in automated biological sampleprocessing instruments allow for quicker, more efficient, and highthroughput analysis of samples. These types of systems may assay agreater number of samples than previous systems. As such, samplesundergoing various assays are labeled or marked with identifiers.

Previously, an operator of the system or instrument may have had tomanually track and validate samples by reading the identifiers on samplecontainers, racks, or assay chips. This type of manual tracking andvalidation can be labor-intensive and include a high probability ofoperator error such as sample mistracking, or improper testing.Furthermore, the greater number of samples desired to be assayed wouldbe more time intensive and cumbersome.

Other more automated systems may scan for identifiers to track andvalidate samples before testing. However, these systems often needadditional components. Furthermore, the identifiers may be misread orunreadable by the systems.

SUMMARY

In one exemplary embodiment, a computer-implemented method foridentifying a first object-of-interest is provided. The firstobject-of-interest includes two identifiers and a sample portion. Themethod includes imaging the first object-of-interest including the twoidentifiers. The imaging generates a first set of image data. The methodfurther includes determining a position of the first object-of-interestin the field-of-view of an optical sensor and determining the twoidentifiers from the first set of image data. The method includesidentifying the first object-of-interest based on the two identifiers.

DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an exemplary object-of-interest according to variousembodiments described herein;

FIG. 2 is a block diagram that illustrates a computer system, upon whichembodiments of the present teachings may be implemented;

FIG. 3 is a block diagram that illustrates an exemplary instrument, uponwhich embodiments of the present teachings may be implemented;

FIG. 4 illustrates an exemplary optical system for imaging according tovarious embodiments described herein;

FIG. 5 illustrates an image of objects-of-interest and associatedidentifiers according to various embodiments described herein;

FIG. 6 illustrates a flowchart of a method for identifying objects byimage analysis according to various embodiments described herein;

FIG. 7 illustrates an identifier according to various embodimentsdescribed herein;

FIG. 8A illustrates another identifier according to various embodimentsdescribed herein;

FIG. 8B illustrates the identifier shown in FIG. 8A after preprocessingaccording to various embodiments described herein; and

FIGS. 9A and 9B illustrate an exemplary workflow according toembodiments described herein.

DETAILED DESCRIPTION

Exemplary systems for methods related to the various embodimentsdescribed in this document include those described in U.S. ProvisionalPatent Application No. 61/541,453, U.S. Provisional Patent ApplicationNo. 61/541,515, U.S. Provisional Patent Application No. 61/541,342, U.S.Provisional Patent Application No. 29/403,049, U.S. Provisional PatentApplication No. 61/541,495, U.S. Provisional Patent Application No.61/541,366, and U.S. Provisional Patent Application No. 61/541,371, allof which are filed Sep. 30, 2011, and all of which are also incorporatedherein in their entirety by reference. Exemplary systems for methodsrelated to the various embodiments described in this document includethose described in U.S. Provisional Patent Application No. 61/660,343,filed Jun. 15, 2012, which is also incorporated herein in its entiretyby reference.

To provide a more thorough understanding of the present invention, thefollowing description sets forth numerous specific details, such asspecific configurations, parameters, examples, and the like. It shouldbe recognized, however, that such description is not intended as alimitation on the scope of the present invention, but is intended toprovide a better description of the exemplary embodiments.

As described above, as the number of items a system needs to process areincreased, a more automated system is desired. Furthermore, moreaccurate and efficient identification, tracking, validation, security,and checking for compatibility, for example, are also desired. As such,samples and assays are often labeled with machine-readable identifiers.Examples of identifiers could be alphanumeric characters or a barcode.

The present application relates to reading identifiers on anobject-of-interest and, more particularly, to identifying an object byanalyzing an image of identifiers on the object-of-interest.

A barcode is a machine-readable representation of data about the objectit labels, usually in the form of varying widths and spacing of parallellines. One type of barcode, barcode 128 encodes character ASCII andsymbols using bars and spaces. The encoding is based on the width ofeach bar and space. Each bar or space has a width that varies betweenone and four units. Every character is encoded in three bars and threespaces, and the width totals in 11 units.

It should be recognized that the methods and systems described hereinmay be implemented in various types of systems, instruments, andmachines. For example, various embodiments may be implemented in aninstrument that performs polymerase chain reactions (PCR) on a pluralityof samples.

According to embodiments described herein, a substrate including one ora plurality of samples within one or a plurality of reaction sites maybe marked with more than one identifier. Having at least two identifiersto read provides more confirmation that the identifier reading isaccurate. In some cases, an identifier may not be able to be detected bythe system. In other cases, an identifier may be inaccurately read andidentify an incorrect sample or assay. By having more than oneidentifier, an extra check can be made to identify the sample.

In various embodiments, the devices, instruments, systems, and methodsdescribed herein may be used to detect one or more types of biologicalcomponents of interest. These biological components of interest may beany suitable biological target including, but are not limited to, DNAsequences (including cell-free DNA), RNA sequences, genes,oligonucleotides, molecules, proteins, biomarkers, cells (e.g.,circulating tumor cells), or any other suitable target biomolecule.

In various embodiments, such biological components may be used inconjunction with various PCR, qPCR, and/or dPCR methods and systems inapplications such as fetal diagnostics, multiplex dPCR, viral detectionand quantification standards, genotyping, sequencing validation,mutation detection, detection of genetically modified organisms, rareallele detection, and copy number variation. Embodiments of the presentdisclosure are generally directed to devices, instruments, systems, andmethods for monitoring or measuring a biological reaction for a largenumber of small volume samples. As used herein, samples may be referredto as sample volumes, or reactions volumes, for example.

While generally applicable to quantitative polymerase chain reactions(qPCR) where a large number of samples are being processed, it should berecognized that any suitable PCR method may be used in accordance withvarious embodiments described herein. Suitable PCR methods include, butare not limited to, digital PCR, allele-specific PCR, asymmetric PCR,ligation-mediated PCR, multiplex PCR, nested PCR, qPCR, genome walking,and bridge PCR, for example.

As described below, in accordance with various embodiments describedherein, reaction sites may include, but are not limited to,through-holes, wells, indentations, spots, cavities, sample retainmentregions, and reaction chambers, for example.

Furthermore, as used herein, thermal cycling may include using a thermalcycler, isothermal amplification, thermal convection, infrared mediatedthermal cycling, or helicase dependent amplification, for example. Insome embodiments, the chip may be integrated with a built-in heatingelement. In various embodiments, the chip may be integrated withsemiconductors.

According to various embodiments, detection of a target may be, but isnot limited to, fluorescence detection, detection of positive ornegative ions, pH detection, voltage detection, or current detection,alone or in combination, for example.

Various embodiments described herein are particularly suited for digitalPCR (dPCR). In digital PCR, a solution containing a relatively smallnumber of a target polynucleotide or nucleotide sequence may besubdivided into a large number of small test samples, such that eachsample generally contains either one molecule of the target nucleotidesequence or none of the target nucleotide sequence. When the samples aresubsequently thermally cycled in a PCR protocol, procedure, orexperiment, the sample containing the target nucleotide sequence areamplified and produce a positive detection signal, while the samplescontaining no target nucleotide sequence are not amplified and produceno detection signal. Using Poisson statistics, the number of targetnucleotide sequences in the original solution may be correlated to thenumber of samples producing a positive detection signal. Positive andnegative detection can be determined or validated by amplificationquality metrics according to various embodiments of the presentteachings.

Substrate

In various embodiments, a substrate may have a plurality of sampleregions, or reaction sites, configured for receiving a plurality ofsamples, wherein the reaction sites may be sealed within the substratevia a lid between the reaction sites and heated cover 210. Some examplesof a sample holder may include, but are not limited to, a multi-wellplate, such as a standard microtiter 96-well plate, a 384-well plate, amicrocard, a through-hole array, or a substantially planar holder, suchas a glass or plastic slide. The reaction sites in various embodimentsof a sample holder may include depressions, indentations, ridges, andcombinations thereof, patterned in regular or irregular arrays formed onthe surface of the sample holder substrate.

According to various embodiments of the present teachings, each reactionsites may have a volume of about 1.3 nanoliters. Alternatively, thevolume each reaction site may be less than 1.3 nanoliters. This may beachieved, for example, by decreasing the diameter of reaction site 104and/or the thickness of the sample holder. For example, each reactionsite 104 may have a volume that is less than or equal to 1 nanoliter,less than or equal to 100 picoliters, less than or equal to 30picoliters, or less than or equal to 10 picoliters. In otherembodiments, the volume some or all of the reaction sites 104 is in arange of 1 to 20 nanoliters.

In some embodiments, the reaction sites are through-holes. In theseexamples, each through-hole has a volume of about 1.3 nanoliters.Alternatively, the volume each through-hole may be less than 1.3nanoliters. This may be achieved, for example, by decreasing thediameter of through-hole and/or the thickness of the sample holdersubstrate. For example, each through-hole may have a volume that is lessthan or equal to 1 nanoliter, less than or equal to 100 picoliters, lessthan or equal to 30 picoliters, or less than or equal to 10 picoliters.In other embodiments, the volume some or all of the through-holes is ina range of 1 to 20 nanoliters.

In various embodiments, a density of reaction sites 104 may be at least100 reaction sites per square millimeter. In other embodiments, theremay be higher densities of reaction sites. For example, a density ofreaction sites 104 within chip 100 may be greater than or equal to 150reaction sites per square millimeter, greater than or equal to 200reaction sites per square millimeter, greater than or equal to 500reaction sites per square millimeter, greater than or equal to 1,000reaction sites per square millimeter, greater than or equal to 10,000reaction sites per square millimeter.

In some embodiments, the reaction sites are through-holes. Accordingly,a density of through-holes within a sample holder substrate may begreater than or equal to 150 through-holes per square millimeter,greater than or equal to 200 through-holes per square millimeter,greater than or equal to 500 through-holes per square millimeter,greater than or equal to 1,000 through-holes per square millimeter,greater than or equal to 10,000 through-holes per square millimeter.

FIG. 1 illustrates a substrate 100 labeled with two identifiersaccording to various embodiments. A plurality of samples may be includedin the reaction site area 102 for testing on a single substrate 100. Thereaction site area 102 is illustrated as an array. In other examples, areaction site area may include one sample. In some embodiments, aplurality of substrates 100 may be in a system for testing. For example,two, four, or twenty substrates 100 may be put in an instrument systemfor testing. The assay components may also be preloaded along with thesample in the reaction sites in some embodiments.

The machine-readable identifiers in the embodiment shown in FIG. 1 are abarcode 104 and an alphanumeric code 106. However, it should berecognized that the machine-readable identifiers, according toembodiments described in this document, may be barcodes, text, numerals,QR codes, or other symbols, for example, and any combination thereof. Itshould also be noted that an alphanumeric identifier is alsohuman-readable and can be validated against a machine-read result.

Computer-Implemented System

Methods of detection and processing of identifiers in accordance withembodiments described herein, may be implemented in a computer system.

Those skilled in the art will recognize that the operations of thevarious embodiments may be implemented using hardware, software,firmware, or combinations thereof, as appropriate. For example, someprocesses can be carried out using processors or other digital circuitryunder the control of software, firmware, or hard-wired logic. (The term“logic” herein refers to fixed hardware, programmable logic and/or anappropriate combination thereof, as would be recognized by one skilledin the art to carry out the recited functions.) Software and firmwarecan be stored on non-transitory computer-readable media. Some otherprocesses can be implemented using analog circuitry, as is well known toone of ordinary skill in the art. Additionally, memory or other storage,as well as communication components, may be employed in embodiments ofthe invention.

FIG. 2 is a block diagram that illustrates a computer system 200 thatmay be employed to carry out processing functionality, according tovarious embodiments. Instruments to perform experiments may be connectedto the exemplary computing system 200. According to various embodiments,the instruments that may be utilized are a thermal cycler system 200 ofFIG. 2 or a thermal cycler system 300 of FIG. 3 may utilize. Computingsystem 200 can include one or more processors, such as a processor 204.Processor 204 can be implemented using a general or special purposeprocessing engine such as, for example, a microprocessor, controller orother control logic. In this example, processor 204 is connected to abus 202 or other communication medium.

Further, it should be appreciated that a computing system 200 of FIG. 2may be embodied in any of a number of forms, such as a rack-mountedcomputer, mainframe, supercomputer, server, client, a desktop computer,a laptop computer, a tablet computer, hand-held computing device (e.g.,PDA, cell phone, smart phone, palmtop, etc.), cluster grid, netbook,embedded systems, or any other type of special or general purposecomputing device as may be desirable or appropriate for a givenapplication or environment. Additionally, a computing system 200 caninclude a conventional network system including a client/serverenvironment and one or more database servers, or integration withLIS/LIMS infrastructure. A number of conventional network systems,including a local area network (LAN) or a wide area network (WAN), andincluding wireless and/or wired components, are known in the art.Additionally, client/server environments, database servers, and networksare well documented in the art. According to various embodimentsdescribed herein, computing system 200 may be configured to connect toone or more servers in a distributed network. Computing system 200 mayreceive information or updates from the distributed network. Computingsystem 200 may also transmit information to be stored within thedistributed network that may be accessed by other clients connected tothe distributed network.

Computing system 200 may include bus 202 or other communicationmechanism for communicating information, and processor 204 coupled withbus 202 for processing information.

Computing system 200 also includes a memory 206, which can be a randomaccess memory (RAM) or other dynamic memory, coupled to bus 202 forstoring instructions to be executed by processor 204. Memory 206 alsomay be used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by processor204. Computing system 200 further includes a read only memory (ROM) 208or other static storage device coupled to bus 202 for storing staticinformation and instructions for processor 204.

Computing system 200 may also include a storage device 210, such as amagnetic disk, optical disk, or solid state drive (SSD) is provided andcoupled to bus 202 for storing information and instructions. Storagedevice 210 may include a media drive and a removable storage interface.A media drive may include a drive or other mechanism to support fixed orremovable storage media, such as a hard disk drive, a floppy disk drive,a magnetic tape drive, an optical disk drive, a CD or DVD drive (R orRW), flash drive, or other removable or fixed media drive. As theseexamples illustrate, the storage media may include a computer-readablestorage medium having stored therein particular computer software,instructions, or data.

In alternative embodiments, storage device 210 may include other similarinstrumentalities for allowing computer programs or other instructionsor data to be loaded into computing system 200. Such instrumentalitiesmay include, for example, a removable storage unit and an interface,such as a program cartridge and cartridge interface, a removable memory(for example, a flash memory or other removable memory module) andmemory slot, and other removable storage units and interfaces that allowsoftware and data to be transferred from the storage device 210 tocomputing system 200.

Computing system 200 can also include a communications interface 218.Communications interface 218 can be used to allow software and data tobe transferred between computing system 200 and external devices.Examples of communications interface 218 can include a modem, a networkinterface (such as an Ethernet or other NIC card), a communications port(such as for example, a USB port, a RS-232C serial port), a PCMCIA slotand card, Bluetooth, etc. Software and data transferred viacommunications interface 218 are in the form of signals which can beelectronic, electromagnetic, optical or other signals capable of beingreceived by communications interface 218. These signals may betransmitted and received by communications interface 218 via a channelsuch as a wireless medium, wire or cable, fiber optics, or othercommunications medium. Some examples of a channel include a phone line,a cellular phone link, an RF link, a network interface, a local or widearea network, and other communications channels.

Computing system 200 may be coupled via bus 202 to a display 212, suchas a cathode ray tube (CRT) or liquid crystal display (LCD), fordisplaying information to a computer user. An input device 214,including alphanumeric and other keys, is coupled to bus 202 forcommunicating information and command selections to processor 204, forexample. An input device may also be a display, such as an LCD display,configured with touchscreen input capabilities. Another type of userinput device is cursor control 216, such as a mouse, a trackball orcursor direction keys for communicating direction information andcommand selections to processor 204 and for controlling cursor movementon display 212. This input device typically has two degrees of freedomin two axes, a first axis (e.g., x) and a second axis (e.g., y), thatallows the device to specify positions in a plane. A computing system200 provides data processing and provides a level of confidence for suchdata. Consistent with certain implementations of embodiments of thepresent teachings, data processing and confidence values are provided bycomputing system 200 in response to processor 204 executing one or moresequences of one or more instructions contained in memory 206. Suchinstructions may be read into memory 206 from another computer-readablemedium, such as storage device 210. Execution of the sequences ofinstructions contained in memory 206 causes processor 204 to perform theprocess states described herein. Alternatively hard-wired circuitry maybe used in place of or in combination with software instructions toimplement embodiments of the present teachings. Thus implementations ofembodiments of the present teachings are not limited to any specificcombination of hardware circuitry and software.

The term “computer-readable medium” and “computer program product” asused herein generally refers to any media that is involved in providingone or more sequences or one or more instructions to processor 204 forexecution. Such instructions, generally referred to as “computer programcode” (which may be grouped in the form of computer programs or othergroupings), when executed, enable the computing system 200 to performfeatures or functions of embodiments of the present invention. These andother forms of non-transitory computer-readable media may take manyforms, including but not limited to, non-volatile media, volatile media,and transmission media. Non-volatile media includes, for example, solidstate, optical or magnetic disks, such as storage device 210. Volatilemedia includes dynamic memory, such as memory 206. Transmission mediaincludes coaxial cables, copper wire, and fiber optics, including thewires that comprise bus 202.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, PROM, and EPROM, aFLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to processor 204 forexecution. For example, the instructions may initially be carried onmagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computing system 200 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detectorcoupled to bus 202 can receive the data carried in the infra-red signaland place the data on bus 202. Bus 202 carries the data to memory 206,from which processor 204 retrieves and executes the instructions. Theinstructions received by memory 206 may optionally be stored on storagedevice 210 either before or after execution by processor 204.

It will be appreciated that, for clarity purposes, the above descriptionhas described embodiments of the invention with reference to differentfunctional units and processors. However, it will be apparent that anysuitable distribution of functionality between different functionalunits, processors or domains may be used without detracting from theinvention. For example, functionality illustrated to be performed byseparate processors or controllers may be performed by the sameprocessor or controller. Hence, references to specific functional unitsare only to be seen as references to suitable means for providing thedescribed functionality, rather than indicative of a strict logical orphysical structure or organization.

PCR Instruments

As mentioned above, an instrument that may be utilized according tovarious embodiments, but is not limited to, is a polymerase chainreaction (PCR) instrument. FIG. 3 is a block diagram that illustrates aPCR instrument 300, upon which embodiments of the present teachings maybe implemented. PCR instrument 300 may include a heated cover 310 thatis placed over a plurality of samples 312 contained in a substrate (notshown). In various embodiments, a substrate may be a glass or plasticslide with a plurality of sample regions, which sample regions have acover between the sample regions and heated cover 310. Some examples ofa substrate may include, but are not limited to, a multi-well plate,such as a standard microtiter 96-well, a 384-well plate, or a microcard,or a substantially planar support, such as a glass or plastic slide. Thereaction sites in various embodiments of a substrate may includedepressions, indentations, ridges, and combinations thereof, patternedin regular or irregular arrays formed on the surface of the substrate.Various embodiments of PCR instruments include a sample block 314,elements for heating and cooling 316, a heat exchanger 318, controlsystem 320, and user interface 322. Various embodiments of a thermalblock assembly according to the present teachings comprise components314-318 of PCR instrument 300 of FIG. 3.

Real-time PCR instrument 300 has an optical system 324. In FIG. 3, anoptical system 324 may have an illumination source (not shown) thatemits electromagnetic energy, an optical sensor, detector, or imager(not shown), for receiving electromagnetic energy from samples 312 in asubstrate, and optics 340 used to guide the electromagnetic energy fromeach DNA sample to the imager. For embodiments of PCR instrument 300 inFIG. 3 and real-time PCR instrument 300 in FIG. 3, control system 320,may be used to control the functions of the detection system, heatedcover, and thermal block assembly. Control system 320 may be accessibleto an end user through user interface 322 of PCR instrument 300 in FIG.3 and real-time PCR instrument 300 in FIG. 3. Also a computer system200, as depicted in FIG. 2, may serve as to provide the control thefunction of PCR instrument 300 in FIG. 3, as well as the user interfacefunction. Additionally, computer system 200 of FIG. 2 may provide dataprocessing, display and report preparation functions. All suchinstrument control functions may be dedicated locally to the PCRinstrument, or computer system 200 of FIG. 2 may provide remote controlof part or all of the control, analysis, and reporting functions, aswill be discussed in more detail subsequently.

The following descriptions of various implementations of the presentteachings have been presented for purposes of illustration anddescription. It is not exhaustive and does not limit the presentteachings to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompracticing of the present teachings. Additionally, the describedimplementation includes software but the present teachings may beimplemented as a combination of hardware and software or in hardwarealone. The present teachings may be implemented with bothobject-oriented and non-object-oriented programming systems.

Optical System for Imaging

FIG. 4 depicts an exemplary optical system 400 that may be used forimaging according to embodiments described herein. It should berecognized that optical system 400 is an exemplary optical system andone skilled in the art would recognize that other optical systems may beused to image an object-of-interest. According to various embodiments,an object of interest may be a substrate as described herein. An opticalsensor 402 included in a camera 404, for example, may image anobject-of-interest 410. Object-of-interest may be an array chip, aplurality of array chips, or a substrate including the sample forassaying, for example. The optical sensor 402 may be a CCD sensor andthe camera 404 may be a CCD camera. Further, the optical sensor includesa camera lens 406.

Depending on the object of interest, an emission filter 408 is chosenfor imagining the object-of-interest 410 according to variousembodiments. Emission filter 408 may be changed to image fluorescentemission emitted from the object-of-interest 401 in other embodiments.

Optical system 400 may use a reflected light source 412 to imageobject-of-interest 410. The light from light source 412 may be filteredthrough an asphere 414, a focuser/diverger 416, and excitation filter418 before being reflected to the object-of-interest 410 by beamsplitter420. Optical system 400 may also include a field lens 422.

Image

As described above, optical system 400 as depicted in FIG. 4, may imagean object-of-interest. The object-of-interest may be a substrate. Asubstrate may be a consumable, such as an array chip (as depicted inFIG. 1). The object-of-interest may also be a plurality of substrates,as depicted in FIG. 5. In exemplary image 500 depicted in FIG. 5, foursubstrates 502, 504, 506, and 508 are included in image 500. It shouldbe recognized that an image may include one or more objects according tovarious embodiments described herein. Each substrate includes twoidentifiers. For example, substrate 502 is marked with identifier 510and identifier 512. As mentioned above, identifiers may be a symbol, analphanumeric code, or a barcode, for example. In image 500, substrate502 has an identifier 510 as an alphanumeric code and identifier 512 asa barcode. In accordance with other embodiments, an object that isimaged may include identifiers that are any combination of symbols,alphanumeric codes, or bar codes, for example.

After an optical sensor 402 (FIG. 4) images at least one objectincluding an identifier, such as a barcode, the image is analyzed toread the identifiers. For example, a barcode identifier is read usingthe image in contrast to a scanning method.

The identifiers may be used to identifier the assays included on thesubstrate, or object. The assay type associated with the identifier maybe stored in a database, or memory, such as memory 206 (FIG. 2). If anobject is labeled with two identifiers, each identifier may beindependently determined according to embodiments described herein. Inthis case, each identifier determination may be validated by determiningif the other identifier is associated. Identifier associations may bestored in memory 206 (FIG. 2) of computer system 200. In otherembodiments, after determining the identifiers and associated assaytypes of the plurality of substrates in the image, compatibility ofassay types of being tested in the same run may be determined.

For example, if substrates 502, 504, 506, and 508 (FIG. 5) were to betested in the PCR instrument 300 (FIG. 3), after identifying substrates502, 504, 506, and 508 from the image 500 (FIG. 5), processor 204 (FIG.2) may determine if the assay types associated with substrates 502, 504,506, and 508 are compatible.

Furthermore, if any identifier in image 500 cannot be read, this mayindicate an substrate is not in the correct position. The system maythen indicate incorrect positioning or an incorrect substrate to theuser if the system cannot identify an substrate, such as displaying anindication on a user interface 322 (FIG. 3).

FIG. 6 illustrates a flowchart depicting an exemplary method 600 ofidentifying an object-of-interest according to embodiments describedherein. The steps of method 600 may be implemented by a processor 204,as shown in FIG. 2. Furthermore, instructions for executing the methodby processor 204 may be stored in memory 206.

With reference to FIG. 6, in step 602, the object-of-interest may beimaged. The object-of-interest includes two identifiers. The twoidentifiers are imaged along with the object-of-interest. Theobject-of-interest may be imaged with an optical system 400, asdescribed above. In response to the imaging of the object-of-interest,image data is generated. The image data may be stored in memory 206(FIG. 2).

Determining a Position of Object-of-Interest within Image

With reference to FIG. 6, in step 604, the object-of-interest may bepositioned in the field-of-view of an optical sensor. Further, alsomentioned above, the object-of-interest may be an substrate according toembodiments of the teachings described herein. By positioning theobject-of-interest adequately, the indicators in an image will be in anexpected position on the image. As such, the image may be analyzed inthe expected position to read the indicators. The expected position maybe within a region-of-interest (ROI).

The expected position of the indicators is further determined bydetermining a point-of-reference within the image. According to variousembodiments, the center of an object-of-interest within the image isdetermined by image analysis techniques. By knowing a point-of-referenceand dimensions of the object-of-interest, expected positions within theimage of the identifiers and other landmarks can be searched.

The position of the object-of-interest may be determined by processor204 (FIG. 2). The sample area 102 (FIG. 1) may be used to determine theROI. Sample area 102, may include wells or holes, for example. Moreparticularly, since the dimensions of the object-of-interest and theposition of the indicators on the object-of-interest are bothpredetermined, sample area can be used to determine the ROI, in whichthe indicators may be found in the image. The ROI may be a shape, suchas a rectangle, where the indicator is expected to be found within theimage. As such, processor 204 may execute instructions to analyze imagedata to read the indicator.

With reference back to FIG. 6, in step 606, the identifiers marked onthe object-of-interest are determined. In other words, the identifiersare read so that the object-of-interest may be identified.

Reading Identifiers

As described above, an indicator may be a barcode. The image data withinthe ROI is processed, or analyzed, by processor 204. The barcode data isdetermined from the image data within the ROI. An example of an imagedbarcode is shown in FIG. 7. The barcode data may be determined byfinding the dark and light zones of the barcode in the image. The imagedata may be filtered in some embodiments to more clearly define the darkand light zones of the barcode.

The dark and light zones can be determined by sampling and filteringmethods known by one skilled in the art. Decisions on whether the imagedata is a dark or light region can be determined by comparing against apredetermined threshold. In other words, the spaces and bars of thebarcode are measured.

The dark and light zones are measured and the barcode can be read. Afteridentifying the barcode from the object-of-interest, the associated datawith the barcode can be determined. The identifier data its associateddata is stored within memory 206. Associated data may include assaytype, or other information such as sample data.

Also described above, an indicator may be an alphanumeric code or othersymbol. Stored in a database or memory 206, for example, templates ofpossible alphanumeric characters or symbols may be stored as binaryimages.

The image may be preprocessed to create more clearly defined dark andlight regions. For example, the gray levels of the image data includingan alphanumeric indicator may be determined. When the gray levels arebelow a certain threshold level, the image data may be determined as alight region. To illustrate the effects of preprocessing, FIG. 8Aillustrates an alphanumeric character before preprocessing. FIG. 8Bdepicts the alphanumeric character after preprocessing.

As described above, an alphanumeric indicator may be included in theROI. The image data within the ROI is parsed and a binary map of eachcharacter can be generated. As such, the determined binary map may becompared to the stored templates of alphanumeric characters. Eachcharacter can be determined by finding a match in the stored templates.In this way, the alphanumeric code can be read. If the indicator is asymbol, the symbol may be determined in a similar manner.

After determining results for both indicators, each indicator result maybe validated with the other indicator result. In other words, thedetermined indicator should be associated with the sameobject-of-interest of the other determined indicator.

Furthermore, as described above, the indicators identify theobject-of-interest, or the substrate, as in step 608 (FIG. 6). The assaytype may be identified. If other substrates are included in the systemfor a test, the assay types of each substrate may be compared to checkfor compatibility. If it is determined that an substrate should not berun with another substrate, an indicator of the error may be displayedto the user on a user interface.

An exemplary workflow, based on reading two identifiers on an object ofinterest, is depicted in FIGS. 9A and 9B. Additionally, two examples ofworkflows, according to various embodiments are described as follows.

From client software (desktop) or instrument LCD panel:

1) User invokes Start Run operation.

2) System prompts user to enter Barcodes for substrates to be run.

3) User physically loads four substrates into instrument.

4) User invokes “Read Barcode” operation from software.

-   -   Option A—User launches Start OA Run workflow from Software:        Pre-Conditions:    -   User loads up to four substrates into instrument    -   User saves Experiment files, Template files or Setup files to        default file location(s) in Software    -   1. Software creates substrate Run object    -   2. Instrument captures multiple CCD images of all substrates,        which includes the barcode and alphanumeric code on each loaded        chip    -   3. For each substrate image:        -   a. Barcode region is derived and parses the image to            determine the Barcode        -   b. Alphanumeric code region is derived and parses the image            to determine the alpha-numeric code        -   c. Barcode and alpha-numeric code results are determined        -   d. Barcode result is validated against alphanumeric code            result        -   e. An experiment file is retrieved from default file            location for alphanumeric code        -   f. If experiment file not found, run setup file retrieved            from default file location for alphanumeric code        -   g. If run setup file found, Uniquely Named Protocol from Run            Setup file is determined and queries/retrieves associated            Run Protocol        -   h. User can manually browse/select alternative experiment            file, run setup file or template file for the substrate        -   i. User can manually browse/select a sample setup file for            the substrate        -   j. System derives experiment filename for substrate; user            can manually change filename    -   4. Run Protocol is applied to the OA Run object    -   5. User invokes Start OA Run operation    -   6. Run Protocols and existing Experiment files for each loaded        OA is validated based on compatibility        -   a. Error handling condition    -   7. For each loaded substrate, Experiment file is created/updated        with the following information:        -   a. Run Protocol        -   b. Assay assignments to Well Positions        -   c. Sample assignments to Well Positions    -   8. Start OA Run command API is run

Option B—User launches Start substrate run workflow from LCD panel:

Pre-Conditions:

-   -   User loads up to four substrates into instrument    -   User saves experiment files or template files to default file        location(s) in instrument    -   1. Firmware creates substrate Run object    -   2. Instrument captures multiple CCD images of all substrates,        which includes the Barcode and Chip ID on each loaded chip    -   3. For each Chip Image:        -   a. Barcode region is derived and parses the image to            determine the Barcode        -   b. Alphanumeric code region is derived and parses the image            to determine the alpha-numeric code        -   c. Barcode and alphanumeric code results are returned        -   d. Barcode result is validated against alphanumeric code            result        -   e. Experiment file from default file location is retrieved            for alphanumeric code        -   f. User can manually browse/select alternative experiment            file or template file for the substrate        -   g. Experiment filename for substrate is derived; user can            manually change filename    -   4. Run Protocol is derived for the substrate Run object    -   5. User invokes Start substrate Run operation    -   6. Run Protocols and existing experiment files for each loaded        substrate are validated for compatibility        -   a. Error handling condition    -   7. For each loaded substrate, experiment file with the following        information is created/updated:        -   a. Run Protocol        -   b. Assay assignments to well positions        -   c. Sample assignments to well positions    -   8. Firmware starts substrate run

Therefore, according to the above, some examples of the disclosurecomprise the following:

In one example, a computer-implemented method for identifying a firstobject-of-interest, wherein the first object-of-interest includes twoidentifiers and a sample portion, is provided. The method comprises:imaging the first object-of-interest including the two identifiers,wherein the imaging generates a first set of image data; determining aposition of the first object-of-interest in the field-of-view of anoptical sensor; determining the two identifiers from the first set ofimage data; and identifying the first object-of-interest based on thetwo identifiers.

Additionally or alternatively, in one or more of the examples disclosedabove, the computer-implemented method of claim 1 further comprises:imaging a second object-of-interest, wherein the secondobject-of-interest includes two identifiers, wherein the imaginggenerates a second set of image data; determining a position of a secondsubstrate in the field-of-view of the optical sensor, wherein the secondsubstrate includes two identifiers and a sample portion; determining thetwo identifiers of the second substrate; and identifying the secondsubstrate based on the two identifiers of the second object-of-interest.

Additionally or alternatively, in one or more of the examples disclosedabove, the first and second object-of-interest is a first and secondsubstrate.

Additionally or alternatively, in one or more of the examples disclosedabove, the computer-implemented method, further comprises: determiningcompatibility of testing the first and second substrates in a samesample run.

Additionally or alternatively, in one or more of the examples disclosedabove, one of the two identifiers is an alpha-numeric code.

Additionally or alternatively, in one or more of the examples disclosedabove, one of the two identifiers is a barcode.

Additionally or alternatively, in one or more of the examples disclosedabove, the identifying includes independent validation of the firstsubstrate based on each of the two identifiers.

Additionally or alternatively, in one or more of the examples disclosedabove, the positioning is based on at least one identifier being withina predetermined region of the field-of-view of the optical sensor.

In another example, a computer-readable storage medium encoded withinstructions, executable by a processor, identifying a firstobject-of-interest, wherein the first object-of-interest includes twoidentifiers and a sample portion, is provided. The instructionscomprising instructions for: imaging the first object-of-interestincluding the two identifiers, wherein the imaging generates a first setof image data; determining a position of the first object-of-interest inthe field-of-view of an optical sensor; determining the two identifiersfrom the first set of image data; and identifying the firstobject-of-interest based on the two identifiers.

Additionally or alternatively, in one or more of the examples disclosedabove, the instructions further include instructions for: imaging asecond object-of-interest, wherein the second object-of-interestincludes two identifiers, wherein the imaging generates a second set ofimage data; determining a position of a second substrate in thefield-of-view of the optical sensor, wherein the second substrateincludes two identifiers and a sample portion; determining the twoidentifiers of the second substrate; and identifying the secondsubstrate based on the two identifiers of the second object-of-interest.

Additionally or alternatively, in one or more of the examples disclosedabove, the first and second object-of-interest is a first and secondsubstrate.

Additionally or alternatively, in one or more of the examples disclosedabove, the computer-readable medium further comprises instructions fordetermining compatibility of testing the first and second substrates ina same sample run.

Additionally or alternatively, in one or more of the examples disclosedabove, one of the two identifiers is an alpha-numeric code.

Additionally or alternatively, in one or more of the examples disclosedabove, one of the two identifiers is a barcode.

Additionally or alternatively, in one or more of the examples disclosedabove, the identifying includes independent validation of the firstsubstrate based on each of the two identifiers.

Additionally or alternatively, in one or more of the examples disclosedabove, the positioning is based on at least one identifier being withina predetermined region of the field-of-view of the optical sensor.

In another example, a system for identifying a first substrate, whereinthe first substrate includes two identifiers and a sample portion, isprovided. The system comprises: a processor; and a memory encoded withinstructions, executable by the processor, the instructions for: imagingthe first object-of-interest including the two identifiers, wherein theimaging generates a first set of image data; determining a position ofthe first object-of-interest in the field-of-view of an optical sensor;determining the two identifiers from the first set of image data; andidentifying the first object-of-interest based on the two identifiers.

Additionally or alternatively, in one or more of the examples disclosedabove, one of the two identifiers is an alpha-numeric code.

Additionally or alternatively, in one or more of the examples disclosedabove, one of the two identifiers is a barcode.

Although the present invention has been described with respect tocertain exemplary embodiments, examples, and applications, it will beapparent to those skilled in the art that various modifications andchanges may be made without departing from the invention.

What is claimed is:
 1. A computer-implemented method for identifying afirst object-of-interest, wherein the first object-of-interest includestwo identifiers and a sample portion, the method comprising: loading thefirst object-of-interest into an instrument for performing a biologicalanalysis of biological components included in the sample portion;imaging the first object-of-interest including the two identifiers,wherein the imaging generates a first set of image data; determining aposition of the first object-of-interest in a field-of-view of anoptical sensor; determining the two identifiers from the first set ofimage data, wherein one of the two identifiers is a barcode; identifyingthe first object-of-interest based on the two identifiers; and selectinga type of biological analysis of biological components included in thesample portions on the first object-of-interest to perform by theinstrument based on the identification of the two identifiers.
 2. Thecomputer-implemented method of claim 1, further comprising: imaging asecond object-of-interest, wherein the second object-of-interestincludes two identifiers, wherein the imaging generates a second set ofimage data; determining a position of a second object-of-interest in thefield-of-view of the optical sensor, wherein the secondobject-of-interest includes two identifiers and a sample portion;determining the two identifiers of the second object-of-interest; andidentifying the second object-of-interest based on the two identifiersof the second object-of-interest.
 3. The computer-implemented method ofclaim 2, wherein the first and second object-of-interest is a first andsecond substrate.
 4. The computer-implemented method of claim 2, furthercomprising: determining compatibility of testing the first and secondobject-of-interests in a same sample run.
 5. The computer-implementedmethod of claim 1, wherein one of the two identifiers is analpha-numeric code.
 6. The computer-implemented method of claim 1,wherein the identifying includes independent validation of the firstobject-of-interest based on each of the two identifiers.
 7. Thecomputer-implemented method of claim 1, wherein the positioning is basedon at least one identifier being within a predetermined region of thefield-of-view of the optical sensor.
 8. A non-transitorycomputer-readable storage medium encoded with instructions, executableby a processor, identifying a first object-of-interest, wherein thefirst object-of-interest includes two identifiers and a sample portion,the instructions comprising instructions for: determining the firstobject-of-interest is loaded into an instrument for performing abiological analysis of biological components included in the sampleportion; imaging the first object-of-interest including the twoidentifiers, wherein the imaging generates a first set of image data;determining a position of the first object-of-interest in afield-of-view of an optical sensor; determining the two identifiers fromthe first set of image data, wherein one of the two identifiers is abarcode; identifying the first object-of-interest based on the twoidentifiers; and selecting a type of biological analysis of biologicalcomponents included in the sample portions on the firstobject-of-interest to perform by the instrument based on theidentification of the two identifiers.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein one of the twoidentifiers is an alpha-numeric code.
 10. The non-transitorycomputer-readable storage medium of claim 8, wherein the identifyingincludes independent validation of the first object-of-interest based oneach of the two identifiers.
 11. The non-transitory computer-readablestorage medium of claim 8, wherein the positioning is based on at leastone identifier being within a predetermined region of the field-of-viewof the optical sensor.
 12. The non-transitory computer-readable storagemedium of claim 8, wherein the instructions further include instructionsfor: imaging a second object-of-interest, wherein the secondobject-of-interest includes two identifiers, wherein the imaginggenerates a second set of image data; determining a position of a secondsubstrate in the field-of-view of the optical sensor, wherein the secondsubstrate includes two identifiers and a sample portion; determining thetwo identifiers of the second substrate; and identifying the secondsubstrate based on the two identifiers of the second object-of-interest.13. The non-transitory computer-readable storage medium of claim 12,wherein the first and second object-of-interest is a first and secondsubstrate.
 14. The non-transitory computer-readable storage medium ofclaim 12, further comprising: determining compatibility of testing thefirst and second substrates in a same sample run.
 15. A system foridentifying a first substrate, wherein the first substrate includes twoidentifiers and a sample portion, the system comprising: a processor;and a memory encoded with instructions, executable by the processor, theinstructions for: determining a first object-of-interest is loaded intoan instrument for performing a biological analysis of biologicalcomponents included in the sample portion; imaging the firstobject-of-interest including the two identifiers, wherein the imaginggenerates a first set of image data; determining a position of the firstobject-of-interest in a field-of-view of an optical sensor; determiningthe two identifiers from the first set of image data, wherein one of thetwo identifiers is a barcode; identifying the first object-of-interestbased on the two identifiers; and selecting a type of biologicalanalysis of biological components included in the sample portions on thefirst object-of-interest to perform by the instrument based on theidentification of the two identifiers.
 16. The system of claim 15,wherein one of the two identifiers is an alpha-numeric code.